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Principal Component Analysis (PCA) Explained | Built In

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Principal component analysis (PCA) is a widely covered machine learning method on the web. And while there are some great articles about it, many go into too much detail. Below we cover how principal component analysis works in a simple step-by-step way, so everyone can understand it and make use of it — even those without a strong mathematical background. Principal component analysis (PCA) is a dimensionality reduction and machine learning method used to simplify a large data set into a smaller set while still maintaining significant patterns and trends. Principal component analysis can be broken down into five steps. I’ll go through each step, providing logical explanations of what PCA is doing and simplifying mathematical concepts such as standardization, covariance, eigenvectors and eigenvalues without focusing on how to compute them. First, some basic (and brief) background is necessary for context. Principal component analysis, or PCA, is a dimensionality reduction method that is

Image: Shutterstock / Built In UPDATED BY Brennan Whitfield | Jun 23, 2025 REVIEWED BY Sadrach Pierre Summary: Principal Component Analysis (PCA) is a dimensionality reduction method that reduces large data sets into fewer variables while preserving key data trends. It simplifies data by identifying uncorrelated components that capture the most variance, making analysis faster and more efficient. more Principal Component Analysis (PCA) is a dimensionality reduction method that reduces large data sets into fewer variables while preserving key data trends. It simplifies data by identifying uncor

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